The capacity of cellular networks can be increased by employing methods that allow the same frequency to be used for different transmissions, in different areas of the network. These “frequency reuse” methods have a related frequency reuse factor, which is defined as the rate at which the same frequency can be reused in the network. For systems that are not sectorized, the reuse factor is defined as 1/K, where K is the number of cells in the network that cannot use the same frequencies for transmission. According to prevalent types of networks, common values for the frequency reuse factor are ⅓, ¼, 1/7, 1/9 and 1/12.
It should be understood that the concepts of frequency reuse have not been completely unified in the industry. For instance, some reuse schemes are defined in terms of additional variables other than 1/K, as discussed above. By way of example, consider a cellular network according to the IEEE 802.16e standard, also known as a Worldwide Interoperability for Microwave Access (WiMAX) network. For instance, a popular scheme within the WiMAX context incorporates an n, m, k naming convention, where n refers to the number of duplicated cells within the network, m represents the number of sectors within each cell (defined by, for example, the number of directional antennas at each base station (BS)), and k represents the number of frequencies used within each cell. Two common reuse patterns in the WiMAX context are n=1, m=3, and k=1; and n=1, m=3, and k=3, which are referred to as “reuse 1” and “reuse 3,” respectively.
Achieving a frequency reuse factor of one is desirable insomuch as doing so increases network communication capacity. However, in general, achieving such an aggressive reuse factor (i.e., achieving a reuse of or about one) presents its own set of problems, which should be obviated to avoid compromising network capacity. In addition to background noise, a primary problem associated with an aggressive reuse factor is interference from neighboring transmitters near cell or sector boundaries. This problem requires that data packet frame structures be designed to accommodate strong interfering signals along cell or sector boundaries. Otherwise, portions of the data frame structure, e.g., pilot symbols, will collide with one another, causing undue interference even when a cell is lightly-loaded.
Consider again the mobile WiMAX network discussed above where a frequency reuse factor of one (1) is used to maximize network capacity and eliminate tedious cell planning. As mentioned, such an aggressive reuse pattern causes strong inter-cell interference along the cell boundaries. For instance, in a typical 3-tier/19-cell/3-sector environment where no AAS/beamforming gain is applied, downlink communications will typically experience very strong interference such that approximately 50% of the signals will have a SINR of 0 dB or less, 30% of the signals will have a SINR of −5 dB or less, and 10% of the signals will have a SINR of −10 dB or less.
This problem is compounded by the fact that, according to the WiMAX standard, data frame structures are not well-designed to accommodate the inference. That is, according to the WiMAX standard, the PUSC zone of a data frame is a mandatory zone and contains critical information for every frame transmission. However, the pilot symbols of the PUSC zone are constantly broadcast regardless of the traffic load. As such, the PUSC pilot symbols are arranged such that the pilot symbols in different cells collide with one another at a high rate. Even when cells in the network are lightly-loaded, the pilot symbols are subject to heavy interference. As a consequence, the pilot symbols effectively become a communication bottleneck. This has a range of negative effects, including a significant restriction on channel estimation.
While this problem is not exclusive to WiMAX, it is particularly noticeable in the WiMAX context. For example, in other types of communication schemes, pilot symbols can relatively easily be separated from one another in different cells or different sectors. For instance, in CDMA or GSM systems, pilot symbols can be easily separated because of the code structure. In contrast, in an OFDM system (e.g., WiMAX) pilot symbols overlap one another as each are transmitted on specific subcarriers, where the subcarriers themselves overlap one another. This makes it especially difficult to filter out pilot symbols for channel estimation purposes.
In view of the above it can be seen that, in networks such as WiMAX networks, the measurement of pilot channels for channel estimation purposes is restricted by the heavy interference at those pilot channels. Currently available systems that attempt to deal with this problem are limited insomuch as they simply treat all measured interference as noise. In a WiMAX network, the pilot symbols of different cells or sectors are randomly scrambled. As such, pilot interference from neighboring cells or sectors appears as white-noise at the receiver. Known systems that apply traditional Minimum Mean Square Error (MMSE) channel estimator algorithms simply treat the interference as noise and thus are unable to determine what interference is attributable to other transmitters. In these systems, the inference can be reduced, but not by a satisfactory amount. As a practical matter, traditional MMSE channel estimator gains are limited to around 3-5 dB in most cases. So, with a boundary area having an SINR value at or about 0 dB, the improvement signal would be around 3-5 dB, which is barely sufficient to support common modulation schemes, e.g., QPSK coding. According to these types of solutions, channel estimation is not satisfactory because, by simply treating all interference as noise, these solutions fail to leverage the inherent structure of the interfering signals.